Data representation for dynamic precision in neural network cores

Systems for neural network computation are provided. A neural network processor comprises a plurality of neural cores. The neural network processor has one or more processor precisions per activation. The processor is configured to accept data having a processor feature dimension. A transformation c...

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Bibliographische Detailangaben
Hauptverfasser: Myron Flickner, Andrew Stephen Cassidy, Pallab Datta, Hartmut Penner, Dharmendra Modha, Jennifer Klamo, Rathinakumar Appuswamy, Steven Kyle Esser, John Vernon` Arthur, Jun Sawada, Brian Seisho Taba
Format: Patent
Sprache:eng
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Zusammenfassung:Systems for neural network computation are provided. A neural network processor comprises a plurality of neural cores. The neural network processor has one or more processor precisions per activation. The processor is configured to accept data having a processor feature dimension. A transformation circuit is coupled to the neural network processor, and is adapted to: receive an input data tensor having an input precision per channel at one or more features; transform the input data tensor from the input precision to the processor precision; divide the input data into a plurality of blocks, each block conforming to one of the processor feature dimensions; provide each of the plurality of blocks to one of the plurality of neural cores. The neural network processor is adapted to compute, by the plurality of neural cores, output of one or more neural network layers.